Stent detection methods and imaging system interfaces

ABSTRACT

The disclosure relates, in part, to computer-based visualization of stent position within a blood vessel. A stent can be visualized using intravascular data and subsequently displayed as stent struts or portions of a stent as a part of a one or more graphic user interface(s) (GUI). In one embodiment, the method includes steps to distinguish stented region(s) from background noise using an amalgamation of angular stent strut information for a given neighborhood of frames. The GUI can include views of a blood vessel generated using distance measurements and demarcating the actual stented region(s), which provides visualization of the stented region. The disclosure also relates to display of intravascular diagnostic information such as indicators. An indicator can be generated and displayed with images generated using an intravascular data collection system. The indicators can include one or more viewable graphical elements suitable for indicating diagnostic information such as stent information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16,166,520, filed Oct. 22, 2018, which is a continuation of U.S.application Ser. No. 15,157,340, which claims priority to and thebenefit of U.S. Provisional Patent Application No. 62/162,795 filed onMay 17, 2015, U.S. Provisional Patent Application No. 62/196,997 filedon Jul. 25, 2015, U.S. Provisional Patent Application No. 62/322,578filed on Apr. 14, 2016, and which also is a continuation-in-part of U.S.patent application Ser. No. 14/975,516 filed on Dec. 18, 2015, thedisclosures of which are each herein incorporated by reference in theirentirety.

FIELD OF THE INVENTION

The disclosure relates generally to intravascular measurements andfeature detection and related diagnostic methods and devices.

BACKGROUND OF THE INVENTION

Coronary artery disease is one of the leading causes of death worldwide.The ability to better diagnose, monitor, and treat coronary arterydiseases can be of life saving importance. Intravascular opticalcoherence tomography (OCT) is a catheter-based imaging modality thatuses light to peer into coronary artery walls and generate imagesthereof for study. Utilizing coherent light, interferometry, andmicro-optics, OCT can provide video-rate in-vivo tomography within adiseased vessel with micrometer level resolution. Viewing subsurfacestructures with high resolution using fiber-optic probes makes OCTespecially useful for minimally invasive imaging of internal tissues andorgans. This level of detail made possible with OCT allows a clinicianto diagnose as well as monitor the progression of coronary arterydisease. OCT images provide high-resolution visualization of coronaryartery morphology and can be used alone or in combination with otherinformation such as angiography data and other sources of subject datato aid in diagnosis and planning such as stent delivery planning.

OCT imaging of portions of a patient's body provides a useful diagnostictool for doctors and others. For example, imaging of coronary arteriesby intravascular OCT may reveal the location of a narrowing or stenosis.This information helps cardiologists to choose between an invasivecoronary bypass surgery and a less invasive catheter-based proceduresuch as angioplasty or stent delivery. Although a popular option, stentdelivery has its own associated risks.

A stent is a tube-like structure that often is formed from a mesh. Itcan be inserted into a vessel and expanded to counteract a stenoticcondition that constricts blood flow. Stents typically are made of ametal or a polymer scaffold. They can be deployed to the site of astenosis via a catheter. During a cardiovascular procedure, a stent canbe delivered to the stenotic site through a catheter via a guide wire,and expanded using a balloon. Typically, the stent is expanded using apreset pressure to enlarge the lumen of a stenosed vessel. Angiographysystems, intravascular ultrasound systems, OCT systems, in combinationsor alone can be used to facilitate stent delivery planning and stentdeployment.

There are several factors that influence the patient outcome whendeploying stents. In some procedures, the stent should be expanded to adiameter that corresponds to the diameter of adjacent healthy vesselsegments. Stent overexpansion may cause extensive damage to the vessel,making it prone to dissection, disarticulation, and intra-muralhemorrhage. Stent under expansion may inadequately expand the vessel ifthe portions of the stent fail to contact the vessel wall, the risk ofthrombosis may increase. An underinflated or malapposed stent may failto restore normal flow. Once a stent is installed, stent malappositionand under expansion of the stent can result in various problems.

There are other challenges associated with stent placements and relatedprocedures. Visualizing a stent deployment relative to the wall of ablood vessel using an angiography system is challenging to undertake byinspection. Further, reviewing angiography images manually to determinestent position on a per image basis is also prone to error.

In addition, after deploying a stent, a clinician may image thetreatment site to confirm that a stent has been properly deployed.However, background noise caused by, for example, uncleared blood cells,can appear as stent struts in OCT image data, making it difficult toaccurately detect the stent. Sometimes, the clinician can identify thestented region, but requiring user intervention results in significantvariability, is subject to user error, and significantly increases thelength of the procedure. In addition, different stents can havedifferent geometries and mesh patterns which can complicate theirevaluation.

The present disclosure addresses these challenges and others.

BRIEF SUMMARY OF THE INVENTION

In part, the disclosure relates to angiography and intravascular datacollection systems such as OCT and/or IVUS that can be used to planstent delivery or otherwise generate and display diagnostic informationof interest. The disclosure also relates to the generation of variousindicators and the integration of them relative to displays of imagedata. As an example, a longitudinal indicator such as an apposition barcan be used alone or in conjunction with a stent strut indicator andoverlaid on angiography frames co-registered with an intravascular dataset such as a set of OCT scan lines or images generated with respectthereto for diagnostic processes such as stent planning.

In part, the disclosure relates to systems and methods for displayingthe results of data analysis applied to an intravascular data set to theuser of an intravascular data collection system and on angiographysystem in one embodiment. In part, this disclosure describes a graphicuser interface (GUI) that provides user interface and graphic datarepresentations that can be applied to one or more generated images of avessel or angiography images such that regions of interest such as areasof stent apposition and others are easy to find and understand on OCTand angiography images.

In part, the disclosure relates to a data collection system such as anintravascular data collection system suitable for use in cath lab suchas an optical coherence tomography system. In part, the disclosurerelates to a data collection system that includes a processor suitablefor displaying intravascular image data. The image data displayedincludes data or images generated based upon depth measurements. In oneembodiment, the image data is generated using optical coherencetomography. The system can also display a user interface for display ofintravascular information such as data relating to stent malappositionin a longitudinal mode on a per stent strut basis or as a bar havingregions corresponding to stent, no stent, or stent apposition levels ofpotential interest for one or more stents in a vessel.

One or more indicators such as longitudinal indicators, as anon-limiting example, can be generated in response to stent detectionprocessing and lumen boundary detection and displayed relative toangiography, OCT, and IVUS images. These can be viewed by a user to planstent delivery and to inflate or adjust a stent delivery by reviewing aco-registered OCT image and an angiography image with the relevantindicators of interest. In part, the systems and methods describedherein relate to methods of avoiding or reducing likelihood of datamisinterpretations by replacing regions of missing data with anindicator such as hashing, a colored region, or other visual indicator.In this way, an end user is informed when data is missing rather thanmisconstrue a black region as a shadow or side branch. Thus, a missingdata region is coded with an indicator that prevents that region frombeing misconstrued as a sidebranch, stent or other feature of interestto a diagnostician. In one embodiment, the methods can include the stepof displaying an indicia of a stent strut on a graphical user interfaceand displaying an indicia indicative of one or more regions in anintravascular image wherein data was unavailable for display. In oneembodiment, an apposition bar is displayed such that it is intravascularview independent such that the apposition bar is displayed when noindicator or stent containing image is present. In one embodiment, thedisclosure relates to an apposition bar aligned with a stent region, thestented region includes the located stent strut, wherein the appositionbar is rotationally agnostic or persistent.

In part, the disclosure relates to stent detection and shadow detectionin the context of intravascular data sets obtained using a probe suchas, for example, and optical coherence tomography probe or anintravascular ultrasound probe.

In part, the disclosure relates to systems and methods for preciseidentification of metal stent strut offsets, or locations, withinshadows cast in OCT image data. Methods of stent strut detection caninclude accessing a plurality of frames of intravascular imaging data,the plurality of frames comprising optical coherency tomography (OCT)scan lines, identifying a shadow region corresponding to a candidatestent strut, identifying scan lines that correspond to a candidate stentstrut shadow region to generate candidate strut shadow scan lines, andanalyzing the candidate strut shadow scan lines to identify the locationof a stent strut.

Methods of stent strut detection also can include storing a plurality offrames of intravascular imaging data, detecting stent struts in a firstgroup of frames of the plurality of frames, detecting one or more shadowregions in the first group of frames, wherein one or more of the shadowregions is adjacent to a detected stent strut, determining on a pershadow region basis if a given shadow region is a guidewire inducedregion or a side branch induced region to generate a set of candidatestent strut shadow regions, wherein each candidate stent strut shadowregion comprises a shadow boundary, and identifying scan lines of acandidate stent strut shadow region within the shadow boundary.

Methods of the invention can include additional steps or features. Forexample, the methods can include identifying a shadow regioncorresponding to a candidate stent strut by eliminating shadow regionscorresponding to non-stent features. The non-stent features can beselected, for example, from the group consisting of: a guidewire, a sidebranch, and combinations thereof.

The methods can include eliminating candidate strut shadow scan linesthat contain spillage from lumen pixels. The methods can includedetermining a projection across each of the candidate strut shadow scanlines by summing a signal response across the candidate strut shadowscan lines, or a portion or sample of the scan lines. The methods caninclude identifying up to three local maxima in the projection.

The methods can include ranking local maxima based on peak signal intensto generate a peak score. The ranking can be an ordinal ranking, withlocal maxima having higher peak signal intensity receiving a higher peakscore.

The methods can include ranking the local maxima based on proximity tothe blood vessel wall to generate a proximity score. The ranking can bean ordinal ranking, with local maxima closer to the blood vessel wallreceiving a higher proximity score. The methods can include assigning amalapposition score to each local maxima. The malapposition score can bebinary, with malapposed local maxima receiving a score of zero.

The methods can include summing the peak score, the proximity score, andthe malapposition score, wherein the local maximum with the highesttotal score is designated as the location of the stent strut.

The methods can include identifying a plurality shadow regioncorresponding to a candidate stent strut, identifying scan lines thatcorrespond to each candidate stent strut shadow region, and identifying,within each candidate stent strut shadow region, the location of a stentstrut. The methods can include performing a cross-frame analysis tovalidate designated stent struts across multiple optical coherencetomography (OCT) imaging frames.

In part, the disclosure relates to intravascular data collection systemsand angiography systems and the exchange of data between two or more ofthe foregoing and the generation and display of diagnostic informationsuch as indicators. One or more indicators can be generated anddisplayed such as by overlaying or otherwise combining such indicatorswith images generated using an intravascular data collection system. Theindicators can include longitudinal, cross-sectional, and other indictortypes such as one more indicator or graphical elements suitable forindicating diagnostic information of interest. Indicators can be used toguide a user during stent delivery planning and other actions. Thedisclosure also relates to stent detection and shadow detection in thecontext of intravascular data sets obtained using a probe such as, forexample, and optical coherence tomography probe or an intravascularultrasound probe.

The present disclosure relates, in part, to computer-based visualizationof stent position within a blood vessel. A stent can be visualized usingOCT data and subsequently displayed as stent struts or portions of astent as a part of a one or more graphic user interface(s) (GUI).Notably, the invention provides computer algorithms that distinguishstented region(s) from background noise. The GUI can include one or moreviews of a blood vessel generated using OCT distance measurements anddemarcating the actual stented region(s), which provides visualizationof the stented region.

In one embodiment, the disclosure relates to automated detection of oneor more stent regions in a pullback. In one embodiment, an OCT, IVUS, orother intravascular modality is used to collect data during thepullback. In one embodiment, the disclosure relates to automaticdetection of one or more stents in a given pullback and removal of falsepositive strut detections from frames not part of a stent. One object ofsome implementations described herein it to automatically detect thestart and end frame of one or more stents in a given pullback withoutuser input. The algorithm uses an angular metric as a threshold andframe-by-frame strut detections to determine which frame belong to astent and which lie outside the stent regions.

The multi-frame processing algorithm automatically detects one or morestents in a pullback based on the struts detected during the singleframe step. In this step, cross-frame information is brought in toidentify the set of frames belonging to a particular stent and cleaningup false positives in non-stented regions. In one embodiment, the methodincludes the step of removing any detections in the guide catheterframes, determined after identifying position of the guide catheter.

In one embodiment, a one dimensional plot of angular coverage is used asa proxy or threshold for filtering stented from non-stent regions. If aframe does not have expected configuration, the angular coverage will below and that be interpreted as the edge of the stent. The onedimensional plot is used for amalgamating data from one adjacent frameas a local neighborhood. In one embodiment, all of the struts in amulti-frame neighborhood come to together compute the coverage metric.Angles are measured for each strut detected in each frame. In amulti-frame algorithm (such as this), the angular position for thecombined set of struts over a fixed neighborhood is used. In oneembodiment, the method combines detected struts to create a super frameand then perform coverage analysis/filtering for stents on the superframe.

In one embodiment, even though struts from the neighborhood around framek is used to compute the max angular gap and the angular coveragemetric, they are assigned to frame k because that's where theneighborhood window is centered. In one embodiment, the angular metricthreshold is on a per frame basis, neighborhood is one ahead of currentframe and one frame behind the current frame. In one embodiment, thenumber of frames in a neighborhood on either side of a frame underreview can include multiple frames without limitation.

In one embodiment, angles are measured for each strut detected in eachframe. In a multi-frame algorithm implementation, the angular positionfor the combined set of struts over a fixed neighborhood is used. In oneembodiment, a neighborhood of frames is processed relative to theangular metric threshold described herein and then a filtering processof the “in stented region signal” is performed using the threshold. Inone embodiment, the neighborhood includes two frames. In one embodiment,the neighborhood includes three frames. In one embodiment, theneighborhood includes two or more frames.

In one embodiment, the disclosure relates to a non-transitorymachine-readable memory medium encoded with a plurality ofprocessor-executable instructions to perform a method of detecting astented region in a blood vessel, comprising processor instructions toperform one or more of the steps described and depicted herein.

The invention relates, in part, to methods of detecting a stented regionin a blood vessel. The method can include the steps of receiving opticalcoherence tomography data for a stented blood vessel, the opticalcoherence tomography data comprising a plurality of image frames;storing the optical coherence tomography data in a memory device of anintravascular data collection system; analyzing the plurality of imageframes to identify stent struts on a per frame basis; demarcating anangular offset of identified stent struts to create amalgamated angulargap data over a neighborhood of frames of the plurality of image frames;and determining a maximum angular gap between any two adjacent struts inthe neighborhood of frames.

The method can include one or more of the following features. The methodcan include classifying the frame as a stent-containing frame if themaximum angular gap is smaller than a threshold angular gap.

The method can include identifying zones that contain stents byidentifying clusters of adjacent frames containing a maximum angular gapthat is smaller than a threshold angular gap.

The method can include determining a centroid value for the stent bloodvessel and computing the maximum angular gap relative to the vesselcentroid for frame k. The maximum angular gap, θmax,k, for a givenframe, k, is used to calculate an angular gap metric, Ψk, for frame kaccording to the formula

$\Psi_{k} = {1 - {\frac{\theta_{maxk}}{2\pi}.}}$An angular gap metric closer to 1 is indicative of the frame containinga stent.

The method can include classifying the frame as a stent-containing frameif the angular gap metric is larger than a threshold angular gap (e.g.,about 0.25 to about 0.65).

The method can include calculating the angular gap metric for frame kand at least one neighboring frame, k+1. The method can includeiteratively calculating the angular gap metric for successiveneighboring frames.

The method can include repeating one or more of the steps tosequentially classify a plurality of frames in the optical coherencetomography data. The method can include sequentially classifying framesas a stent-containing frame if the angular gap metric for a given frameis larger than a threshold angular gap. The method can includeaggregating neighboring stent-containing frames into a stented regioncomprising a first frame and a last frame. The method can includeterminating a first end of the stented region if a frame adjacent thefirst frame has an angular gap metric below the threshold angular gap.

The method can include terminating a second end of the stented region ifa frame adjacent the last frame has an angular gap metric below thethreshold angular gap. The disclosure also relates, in part, to methodsof detecting a stented region in a stented blood vessel. The method caninclude the steps of storing, using an intravascular imaging system, oneor more intravascular image datasets of the blood vessel, eachintravascular dataset comprising a plurality of frames; storing, usingan intravascular imaging system, one or more intravascular imagedatasets of the blood vessel, each intravascular dataset comprising aplurality of frames; defining a neighborhood, the neighborhoodcomprising a frame k and one or more frames in vicinity of frame k;determining an angular gap for frame k by combining all of strutsdetected on all frames of the neighborhood; and generating an angularcoverage metric Ψk with regard to frame k using the determined angulargap.

The angular coverage metric is of the form

$\Psi_{k} = {1 - \frac{\theta_{maxk}}{2\pi}}$wherein, θmax,k is largest angular gap between adjacent struts.

The method can include sequentially classifying frames as astent-containing frame if the angular coverage metric for a given frameis larger than a threshold angular gap. The disclosure also relates, inpart, to a programmable processor-based computing device of anintravascular imaging system for detecting one or more stented regions.The programmable processor-based computing device can include one ormore data access channels to receive intravascular imaging data; and aprocessor and associated memory in electrical communication with the oneor more data access channels.

In one embodiment, the processor is programmed to store, using anintravascular imaging system, one or more intravascular image datasetsof the blood vessel, each intravascular dataset comprising a pluralityof frames; define a neighborhood, the neighborhood comprising a frame kand one or more frames in vicinity of frame k; determine an angular gapfor frame k by combining all of the struts detected on all frames of theneighborhood; generate an angular coverage metric Ψ_(k) with regard toframe k using the determined angular gap; and classify frames as astent-containing frame if the angular coverage metric for a given frameis larger than a threshold angular gap.

In one embodiment, the disclosure relates to detecting the maximum stentmalapposition distance, defined as the widest separation between thesurface of the stent struts and the vessel wall over the entire lengthof the stent. Minimization of this distance, especially for drug-elutingstents, is necessary to assure that the stent is affixed firmly to thevessel wall and that the stent provides adequate radial support toprevent collapse of the vessel.

In one embodiment, the disclosure relates to detecting maximum stentmalapposition distance, defined as the widest separation between thesurface of the stent struts and the vessel wall over the entire lengthof the stent Minimization of this distance, especially for drug-elutingstents, is necessary to assure that the stent is affixed firmly to thevessel wall and that that the stent provides adequate radial support toprevent collapse of the vessel.

In part, the disclosure relates to a computer interface with a threedimensional depiction in the top panel of a stent that is not properlyplaced in the lumen of interest. Regions of stent malapposition can beshown as hatched regions or with other indicia. Thus, in one embodiment,the methods of the invention and features described herein are directedto a computer-based user interface that allows views of OCT in multiplepanels. Further, stent malapposition can be shown in three-dimensions.In addition, in the case of stimulated stent placement, the user mayreposition the stent to remove the areas of malapposition to simulateproper stent placement prior to implanting a stent in a real patient.

The methods can include displaying on a graphical user interface thevalidated stent struts. The disclosure also includes a computer readablemedium comprising non-transitory instructions that when executed cause aprocessor to perform any of the foregoing steps.

Although, the invention relates to different aspects and embodiments, itis understood that the different aspects and embodiments disclosedherein can be integrated together as a whole or in part, as appropriate.Thus, each embodiment disclosed herein can be incorporated in each ofthe aspects to varying degrees as appropriate for a givenimplementation. Furthermore, although some aspects and embodiments aredescribed using “means for” terminology, it is understood that allaspects, embodiments, and other concepts disclosed herein can serve assupport for means plus function claims, even if specific “means for”language is not used in a specific portion of the written description.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings) will be provided by the Office upon request and paymentof the necessary fee.

The figures are not necessarily to scale, emphasis instead generallybeing placed upon illustrative principles. The figures are to beconsidered illustrative in all aspects and are not intended to limit thedisclosure, the scope of which is defined only by the claims.

FIG. 1 shows a schematic, diagram of an intravascular imaging and datacollection system in accordance with an illustrative embodiment of thedisclosure.

FIGS. 2A-2E show additional details relating to user interface displaysand intravascular data, collection systems and indicators suitabletherewith and angiography systems for diagnostic processes includingstent delivery planning in accordance with an illustrative embodiment ofthe disclosure.

FIGS. 3A-6 show various user interfaces and data representationsincluding various indicia and co-registered features relative to one ormore imaging modalities in accordance with an illustrative embodiment ofthe disclosure.

FIGS. 7-7B show a three-dimensional representation of side branchindicators generated using intravascular imaging data such as OCT datain accordance with an illustrative embodiment of the disclosure.

FIGS. 8A-9B show additional details relating to user interface displaysand intravascular data collection systems and indicators suitabletherewith and angiography systems for diagnostic processes.

FIG. 10A is an exemplary intravascular data collection system and anassociated intravascular data collection probe and related imageprocessing, detection, and other software components according to anillustrative embodiment of the disclosure.

FIG. 10B is a cross-sectional OCT image of a stented blood vessel inaccordance with an illustrative embodiment of the disclosure.

FIG. 11 is a process flow chart for detecting struts in OCT image datain accordance with an illustrative embodiment of the disclosure,

FIG. 12 is a scan line OCT image in polar co-ordinates, in log scale, ofa stented vessel in accordance with an illustrative embodiment of thedisclosure.

FIG. 13 is a graph illustrating detection of multiple potential strutswithin a surge shadow in accordance with an illustrative embodiment ofthe disclosure,

FIG. 14A is an intravascular image data representation user interface astented vessel region before elimination of false positive stent strutsin accordance with an illustrative embodiment of the disclosure.

FIG. 14B is an intravascular image data representation user interface ofa stented vessel region after elimination of false positive stent strutsin accordance with an illustrative embodiment of the disclosure.

FIGS. 15A and 15B are diagrammatic depictions of combining frames aspart of an evaluation of whether or not a frame is part of a stentedregion an automated stent detection algorithm in accordance with anillustrative embodiment of the disclosure.

FIG. 16 is graph of an angular coverage plot demarcating the framelocation of two stents in accordance with an illustrative embodiment ofthe disclosure.

FIG. 17 is a flow chart showing a multi-frame stent region detectionalgorithm in accordance with an illustrative embodiment of thedisclosure.

FIG. 18A is an intravascular image data representation user interface ofa stented vessel region before elimination of false positive struts inaccordance with an illustrative embodiment of the disclosure.

FIG. 18B is an intravascular image data representation user interface ofa stented vessel region after elimination of false positive struts inaccordance with an illustrative embodiment of the disclosure.

DETAILED DESCRIPTION

In part, the disclosure relates to intravascular data collectionsystems, such as OCT, IVUS, and angiography systems and the exchange ofdata between two or more of the foregoing, as examples, and thegeneration and display of diagnostic information such as indicators. Inone embodiment, intravascular data such as OCT is collected whileangiography data is simultaneously collected. Indicators can include oneor more one or two dimensional graphic elements and one or moreassociated indicia such as color, gray scale or other scale gradations,hashes, symbols or other visual elements.

One or more indicators can be generated and displayed such as byoverlaying or otherwise combining such indicators with images generatedusing an intravascular data collection system. The indicators caninclude longitudinal, cross-sectional, and other indictor types such asone or more indicia or graphical elements suitable for indicatingdiagnostic information of interest such as tracking relative to userselected landmarks. Stent strut indicators can also be used. Methods ofstent and shadow detection are described herein that can be used todisplay such intravascular features in a user interface and to displayoverlays relative thereto such as indicators or indicia. Angiographydata can also be integrated and displayed with various common indicatorsas part of a co-registered display. In one embodiment, shadows and otherdements which can be misconstrued as dissections, side branches or othervessel features can be shaded or otherwise changed to distinguish themand facilitate user review and analysis of images frames and dataaccording to one embodiment.

Suitable diagnostic information can include stent apposition informationsuch as the malapposition of a stent relative to a vessel wall or lumenboundary, user selected OCT positions in a vessel and associatedangiography frame locations, and other intravascular diagnosticinformation or other information generated to facilitate stent deliveryplanning. The system includes a processor in communication with thegraphical user interface and configured to send commands to thegraphical user interface. One or more software programs are used toperform one or more of the following: co-register data such as frames ofimage data, generate and display longitudinal indicators indicative ofstent position relative to a determined lumen boundary, code or markdata missing regions for an end user, translate user selected OCTposition information to an angiography display using one or moregraphical elements to facilitate co-registration, and visuallyidentifying stents and simulated stents for planning purposes and othersas described herein.

In part, the disclosure relates to a graphical user interface (GUI)element or indicator that is represented on a display relative tosubject data such as image data or other intravascular parametersmeasured relative to the subject. Any clinically useful parameter as itchanges longitudinally or cross-sectionally during the course of anOptical Coherence Tomography pullback recording or IVUS or otherintravascular angiography system can be evaluated and displayed as anindicator or indicia. Each indicator/indicia can be used byinterventional cardiologists to quickly see clinically usefulinformation for an entire pullback recording in a single view withoutneeding to manually manipulate the image. The indicator can guide a userto the particular points of interest in the vessel based on theparameter exceeding or falling below a clinically meaningful thresholdvalue. By encoding the parameter value in a continuous color map, orother scale using suitable indicia for example, varying degrees ofseverity of the parameter can be easily summarized for the entire vesselin one easy to interpret view. These features are shown with the variousapposition bars, stent indicators, and other indicators for angiographyimages and other intravascular data collection images.

FIG. 1 includes a system suitable for performing sonic of thesefeatures. FIG. 2A shows four panels with the top tight panel includingan angiography display with various indicators including a first userselected position US1, a second user selected position US2, and anactive frame AF. These indicators are also shown in the bottom U-mode orlongitudinal panel with US1 and US2 corresponding to the vertical linesshown and the active frame AF corresponding to the vertical line inbetween them. The active frame is shown in cross-sectional view at thetop right panel. The middle panel shows the values of the US1 and US2positions in ram as vessel positions and a calculated MLA. FIG. 2B showsa zoomed view of the angiography image of FIG. 1 which is co-registeredwith the OCT data of FIG. 1 . These user interfaces include moveableelements C1, C2 that can be controlled by a user with a mouse, joystick,or other control and can be operated using one or more processors andmemory storage elements. The movable elements C1, C2 are controls andcan be rotated or moved as part of the interface. They are also shown inFIGS. 2C and 3B and others. In FIG. 3B, the controls C1, C2 are alsorepresented as half circles relative to the stent struts and as linesegments in the right panel.

FIG. 2C shows an apposition bar/indicator bar 111 as an indicator withregions R1 to R7 which are shown in the angiography view in the topright which is shown in further detail in FIG. 2D. The top right panelshows R3 shows an area of apposition that is beyond a threshold ofinterest. In the L-mode, the stent struts are coded with an indicia suchas symbols or color. The regions of apposition of interest in theapposition bar remain on display even if the dataset is rotated in orderto bring the important areas to the attention of a user for stentplanning and patient diagnosis. In this sense, indicators can bepersistent to direct a user's focus during a planning or otherprocedure. FIG. 2E shows additional details relating to an exemplaryapposition bar or indicator bar 111. The indicator bar 111 can be usedfor stent planning and review and to indicate regions in anintravascular image where there is apposition or another metric relativeto a stent strut. The indicator bar 111 is persistent in the userinterface views in one embodiment to alert a user to a stent region evenif it is not visible based on the view selected by the userthree-dimensional, cross-sectional, longitudinal, viewing angle, etc.

With regard to FIG. 2C, the two user selected points of interest areshown as U1 and U2. R3 corresponds to a region of malapposition ofinterest. R2 corresponds to a first stent and R6 corresponds to a secondstent. R5 is the gap in between them. This data is co-registered withangiography data as shown in FIG. 2D to facilitate stent planning. Datacollecting element of probe DC is shown in the image. R1 and R7 aredistal and proximal areas in which no stent is present and correspond tovessel lumen. U1 and U2 serve as user placed landmarks that can be usedby a user looking at live angio to give them a reference frame for thevessel section they marked with U1 and U2. One or more displays can beused such as live angio and OCT pullback data with previously acquiredpullback frames.

With these and other indicators, the images and indicators can help as atool to guide stent delivery based on the data shown in FIGS. 2C and 2D.The indicators can also show when a stent needs to be inflated in moredetail given color coded or otherwise coded stent strut indicia in across-sectional or longitudinal view. In FIG. 2F, regions of a firststent 222 a and a second stent 222 b are shown by the apposition bar111. A region of lumen or no stent 224 and malapposition region 223 arealso shown. This bar 111 can be displayed on any angiography or OCT orIVUS image of interest. In one embodiment, an apposition bar isdisplayed such that it is intravascular view independent or persistentsuch that the apposition bar is displayed when no indicator or stentcontaining image is present. The various indicators and indicia can begenerated based upon stent detection, lumen detection, stent appositionmeasurements and various graphic overlays generated using the system ofFIGS. 1 and 10A, for example.

FIG. 3A shows an interface with a longitudinal view or L-mode showing anapposition bar above stent strut indicators coded based on appositionthresholds. The indicator bar 111 is shown in the middle of the GUI withno stent 224 regions and malapposition regions 223 shown. Lumen boundarydata from OCT or IVUS is used to determine apposition issues such asthresholds being exceeded given detected strut data as inputs to theapposition bar generation software module. In FIG. 3B, the interfacescreen depicts an example of the indicator measuring a high level ofapposition for a metallic stent strut as shown above the L-Mode displayin the GUI screen shot shown. The apposition indicator allows for thesummary information about the clinical parameter to be displayed withoutthe need to manually manipulate or inspect the image data. A stentapposition bar and other indicators shown herein and theirco-registration with angiography offer many advantages to a user.

In FIGS. 3A, 3B, 4A, and 4B some embodiments of user interfacesdepicting intravascular data and angiography data (where applicable)along with an exemplary indicator for stent strut apposition and otherindicator-based data displays. In one embodiment, the apposition bar isshown on L-Mode and angiography images, three-dimensional flow throughimages and others. FIG. 4B shows an angiography image with stent datashowing threshold information along the outer boundary of the vessel aswell as a longitudinal view of the apposition bar 111. A region 161 a inthe angiography portion of the user interface is also aligned with aregion 161 b of the indicator bar 111. In one embodiment, theangiography images are aligned or registered with the apposition bar.One feature of the apposition bar 111 is that it is persistent in theuser interfaces such that if a stent is present in a 2 or 3D image, butit does not appear based on the cut plane or viewing angle, theapposition bar would persist and show that a stent and any associatedmalapposition is present even though a 2D or 3D stent does not appear inthe GUI. This is a useful feature for stent planning an diagnostics.

In FIG. 4A, an indicator bar 111 is shown that indicates stent strutsand apposition areas of interest 157. These areas of interest 157showing apposition can be grouped with a representation of the stentstruts themselves and color coded or coded with another indicia that isviewable in a GUI. An example of a grouping of stent struts code withair indicia and the indicator bar is shown by region 188 in FIGS. 3A,3B, 5 and 6 . In these regions 188, an indicator bar 111 is shownaligned with a representation of a stent and a series of struts withvarious indicia corresponding to apposition relative to the vessel wall.A detected lumen boundary is used to compare stent position relativethereto. Additional details relating to stent detection are includedherein.

FIG. 5 shows another GUI with an indicator or image data processingfeature by which missing data such as data obscured or missing theshadow of a guidewire is modified by software to replace it with a graymask or another indicator. To avoid user confusion with side branches,dissections, or missing data an indicator MD is used to indicate regionswhere data is missing. This has the benefit of preventing a user frommistaking it for missing data, a dissection area, or a side branch. Inone embodiment, areas where data is missing as a result of a shadow orotherwise are displayed with an indicia or indicator such as a grayregion, a colored region, hashing, or another visible indicia. Thedouble headed arrow icon in the middle allows the view to be rotated.This user control along with the apposition bar and identification of aguidewire by color coding or other indicia all improve and extend thediagnostic range of the image data from an intravascular data collectionprobe and/or angiography data.

In another embodiment, as shown in FIG. 5 . an indicator W is used topoint to a guidewire image. In one embodiment, the indicator W can beused to identify a guidewire in the GUI or select it for removal fromimage. FIG. 6 shows a three-dimensional fly though with the appositionbar shown as a trajectory ahead of the viewing plane of the usercorresponding to the cross-section on the right. Indicator bar 111showing stent struts and apposition areas of interest—included in 3D flythought view and any other view of interest—regions of interest remainviewable during rotation to alert user of important vessel regionsduring stent planning.

FIGS. 7A and 7B show other indicators SB corresponding to side branches.A rendering of the vessel wall VW is also shown relative to the sidebranches. These and other indicators can be used to emphasize regions in2D and 3D data. As shown in the user interfaces depicted, such as FIG.9B, the various circles/line segments C1, C2 in the top right view canbe rotated to navigate through various views of the image. FIGS. 8A to9B show additional interfaces and control information for navigatingimage data sets and performing diagnostics such as stent planning.Various proximal and distal views and other perspective views can benavigated using the tools shown herein. In one or more embodiments, theindicators such as apposition bar 111 are persistent such that theyremain in view even if navigated away from an area of malapposition.

Thus, some indicators are rotationally agnostic such that if theindicator includes a region or length that includes a parameter thatexceeds a threshold then that region remains indicated as such even ifthe image data is changed such that the rotated view obscures the regionsuch as a malapposed stent region. Thus, if one side of a vessel has anapposition issue, the user remains aware of it relative to theirlocation in the vessel. The apposition bar can be displayed as anindicator in one or more views of an angiography or OCT image oruser-interfaces.

As shown in the various figures, the apposition bar 111 can besubdivided into various regions or lengths indicative of the presence ofor more stents in the vessel or the malposition or the gap in betweenstents for a multi-stented vessel. The angiography data and associateimage frames can be co-registered with OCT data. Further, as shown inthe figure user selected vertical lines corresponding certainlongitudinal distances on the artery can be set to guide stent planning.The rotational agnostic or persistent nature of the bar provides furtherassistance and error reduction during stem planning.

During a stent delivery planning procedure, clinician specifiedlandmarks can be used for stunt planning by providing a reference for auser to select stunt sizes and relative to the vessel with respect towhich user can refer to while deploying a stent using live angio. Giventhe levels and location of malapposition the user can refer to OCT andannotated angiography to further expand or move a stent as part ofdelivery planning. These system features and methods can be implementedusing system 3 shown in FIG. 1 and the system of FIG. 10A, for example.

FIG. 1 shows a system 3 which includes various data collectionsubsystems suitable for collecting data or detecting a feature of orsensing a condition of or otherwise diagnosing a subject 4. In oneembodiment, the subject is disposed upon a suitable support 19 such astable bed to chair or other suitable support. Typically, the subject 4is the human or another animal having a particular region of interest25.

The data collection system 3 includes a noninvasive imaging system suchas a nuclear magnetic resonance, x-ray, computer aided tomography, orother suitable noninvasive imaging technology. As shown as anon-limiting example of such a noninvasive imaging system, anangiography system 21 such as suitable for generating cines is shown.The angiography system 21 can include a fluoroscopy system. Angiographysystem 21 is configured to noninvasively image the subject 4 such thatframes of angiography data, typically in the form of frames of imagedata, are generated while a pullback procedure is performed using aprobe 30 such that a blood vessel in region 25 of subject 4 is imagedusing angiography in one or more imaging technologies such as OCT orIVUS, for example.

The angiography system 21 is in communication with an angiography datastorage and image management system 22, which can be implemented as aworkstation or server in one embodiment. In one embodiment, the dataprocessing relating to the collected angiography signal is performeddirectly on the detector of the angiography system 21. The images fromsystem 21 are stored and managed by the angiography data storage andimage management 22.

In one embodiment system server 50 or workstation 87 handle thefunctions of system 22. In one embodiment, the entire system 21generates electromagnetic radiation, such as x-rays. The system 21 alsoreceives such radiation after passing through the subject 4. In turn,the data processing system 22 uses the signals from the angiographysystem 21 to image one or more regions of the subject 4 including region25. This system allows the angiography data to be shown on displays 82and 82 along with intravascular data and the various indicators anddetected stent struts and shadows as described herein.

As shown in this particular example, the region of interest 25 is asubset of the vascular or peripherally vascular system such as aparticular blood vessel. This can be imaged using OCT, A catheter-baseddata collection probe 30 is introduced into the subject 4 and isdisposed in the lumen of the particular blood vessel, such as forexample, a coronary artery. The probe 30 can be a variety of types ofdata collection probes such as for example an OCT probe, an FFR probe,an IVUS probe, a probe combining features of two or more of theforegoing, and other probes suitable for imaging within a blood vessel.The probe 30 typically includes a probe tip, one or more radiopaquemarkers, an optical fiber, and a torque wire. Additionally, the probetip includes one or more data collecting subsystems such as an opticalbeam director, an acoustic beam director, a pressure detector sensor,other transducers or detectors, and combinations of the foregoing.

For an intravascular probe that includes an optical beam director, theoptical fiber 33 is in optical communication with the probe with thebeam director. The torque wire defines a bore in which an optical fiberis disposed. In FIG. 1 , the optical fiber 33 is shown without a torquewire surrounding it. In addition, the probe 30 also includes the sheathsuch as a polymer sheath (not shown) which forms part of a catheter. Theoptical fiber 33, which in the context of an OCT system is a portion ofthe sample arm of an interferometer, is optically coupled to a patientinterface unit (PIU) 35 as shown.

The patient interface unit 35 includes a probe connector suitable toreceive an end of the probe 30 and be optically coupled thereto.Typically, the data collection probes 30 are disposable. The PIU 35includes suitable joints and elements based on the type of datacollection probe being used. For example a combination OCT and IVUS datacollection probe requires an OCT and IVUS PIU. The PIU 35 typically alsoincludes a motor suitable for pulling back the torque wire, sheath, andoptical fiber 33 disposed therein as part of the pullback procedure. Inaddition to being pulled back, the probe tip is also typically rotatedby the PIU 35. In this way, a blood vessel of the subject 4 can be islongitudinally or via cross-sections. The probe 30 can also be used tomeasure a particular parameter such as an FFR or other pressuremeasurement. The image data can be used to generate various 2D and 3Dviews which can be navigated as shown in the user interface depictions.

In turn, the PIU 35 is connected to one or more intravascular datacollection systems 42. The intravascular data collection system 42 canbe an OCT system, an IVUS system, another imaging system, andcombinations of the foregoing. For example, the system 42 in the contextof probe 30 being an OCT probe can include the sample arm of aninterferometer, the reference arm of an interferometer, photodiodes, acontrol system, and patient interface unit. Similarly, as anotherexample, in the context of an IVUS system, the intravascular datacollection system 42 can include ultrasound signal generating andprocessing circuitry, noise filters, rotatable joint, motors, andinterface units. In one embodiment, the data collection system 42 andthe angiography system 21 have a shared clock or other timing signalsconfigured to synchronize angiography video frame time stamps and OCTimage frame time stamps.

In addition to the invasive and noninvasive image data collectionsystems and devices of FIG. 1 , various other types of data can becollected with regard to region 25 of the subject and other parametersof interest of the subject. For example, the data collection probe 30can include one or more pressure sensors such as for example a pressurewire. A pressure wire can be used without the additions of OCT orultrasound components. Pressure readings can be obtained along thesegments of a blood vessel in region 25 of the subject 4.

Such readings can be relayed either by a wired connection or via awireless connection. As shown in a fractional flow reserve FFR datacollection system, a wireless transceiver 48 is configured to receivepressure readings from the probe 30 and transmit them to a system togenerate FFR measurements or more locations along the measured bloodvessel. One or more displays 82, 83 can also be used to show anangiography frame of data, an OCT frame, user interfaces for OCT andangiography data, shadows, indicators, missing data and other controlsand features of interest.

The intravascular image data such as the frames of intravascular datagenerated using the data collection probe 30 can be routed to the datacollection processing system 42 coupled to the probe via PIU 35. Thenoninvasive image data generated using angiography system 22 can betransmitted to, stored in, and processed by one or more servers orworkstations such as the co-registration server 50 workstation 87. Avideo frame grabber device 55 such as a computer board configured tocapture the angiography image data from system 22 can be used in variousembodiments.

In one embodiment, the server 50 includes one or more co-registrationsoftware modules 60 that are stored in memory 70 and are executed byprocessor 80. The server 50 can include other typical components for aprocessor-based computing server. Or more databases such as database 90can be configured to receive image data generated, parameters of thesubject, and other information generated, received by or transferred tothe database 90 by one or more of the systems devices or componentsshown in FIG. 1 . Although database 90 is shown connected to server 50while being stored in memory at workstation 87 this is but one exemplaryconfiguration. For example, the software modules 60 can be running on aprocessor at workstation 87 and the database 90 can be located in thememory of server 50. The device or system use to run various softwaremodules are provided as examples. In various combinations the hardwareand software described herein can be used to obtain frames of imagedata, process such image data, and rester such image data.

As otherwise noted herein, the software modules 60 can include softwaresuch as preprocessing software, transforms, matrices, lumen detection,stent detection, shadow detection, indicator generator and display, andother software-based components that are used to process image data orrespond to patient triggers to facilitate co-registration of differenttypes of image data by other software-based components 60 or tootherwise perform such co-registration. The modules can include lumendetection using a scan line based or image based approach, stentdetection using a scan line based or image based approach, indicatorgeneration, apposition bar generation for stent planning, guidewireshadow indicator to prevent confusion with dissention, side branches andmissing data, and others.

The database 90 can be configured to receive and store angiography imagedata 92 such as image data generated by angiography system 21 andobtained by the frame grabber 55 server 50. The database 90 can beconfigured to receive and store OCT image data 95 such as image datagenerated by OCT system 42 and obtained by the frame gabber 55 server50.

In addition, the subject 4 can be electrically coupled via one or moreelectrodes to one more monitors such as, for example, monitor 49.Monitor 49 can include without limitation an electrocardiogram monitorconfigured to generate data relating to cardiac function and showingvarious states of the subject such as systole and diastole. Knowing thecardiac phase can be used to assist the tracking of vessel centerlines,as the geometry of the heart, including the coronary arteries, isapproximately the same at a certain cardiac phase, even over differentcardiac cycles.

Hence, if the angiography data spans a few cardiac cycles, a first-ordermatching of vessel centerline at the same cardiac phase may assist intracking the centerlines throughout the pullback. In addition, as mostof the motion of the heart occurs during the systole, vessel motion isexpected to be higher around the systole, and damp towards the diastole.This provides data to one or more software modules as an indication ofthe amount of motion expected between consecutive angiography frames.Knowledge of the expected motion can be used by one or more softwaremodules to improve the tracking quality and vessel centerline quality byallowing adaptive constraints based on the expected motion.

Shadow Detection Related Embodiments

The disclosure provides, in part, methods and systems for identifyingwithin a detected stent shadow the precise offset, or location, of thestrut resulting in the detected shadow. Sometimes, within a shadow thereis a single possible strut location corresponding to a bright strutbloom, or peak, against a dark shadow background in the scan line.However, multiple strut peaks often are detected inside a strut shadow,making it difficult to identify the exact location of a stent strut.Spurious peaks can be caused by, for example, blood pooling, poor bloodclearing in the pullback zone, or ringing artifacts due to the imagingoptics interacting with the metal strut. The present disclosure providesmethods and systems for identifying the best candidate for a true stentwithin a stent shadow.

FIG. 10A is a high level schematic diagram depicting a blood vessel 5,such as an artery, a data collection probe 7 and an intravascular datacollection and processing system 10. The methods described with regardto system 10 of FIG. 10A can also be performed with system 3 of FIG. 1and other systems. In particular, the system 10 of FIG. 10A. The system10 can include for example, an OCT, intravascular ultrasound (IVUS), orother intravascular imaging system. A stent 12 is shown in the bloodvessel 5. The stent includes a plurality of struts. Some of the strutscan generate shadows or shadow regions SR as part of the process ofimaging the vessel with an intravascular probe. The system 10 caninclude various software modules suitable for performing side branchdetection, stent detection, peak detection, shadow region detection andprocessing, error correction, indicator bar generation and display,model comparisons, lumen detection, and various other processes asdescribed herein. Additional details relating to some exemplary stentdetection features are described in more detail with regard to FIGS.14A-18B. The system 10 can include a suitable light source thatsatisfies the coherence and bandwidth requirements of the applicationsand data collection described herein. The system 10 can include anultrasound imaging system. The probe 7 can include a catheter 20 havinga catheter portion having one or more optical fibers 15 and a probe tip17 disposed therein. The probe tip 17 includes a beam director in oneembodiment.

As shown, the catheter 20 is introduced into the lumen 11 such as anarterial lumen. The probe 7 can include a rotating or slidable fiber 15that directs light forward into the lumen L or at a directionperpendicular to the longitudinal axis of the fiber 15. As a result, inthe case of light that is directed from the side of the probe as thefiber 15 rotates, OCT data is collected with respect to the walls of theblood vessel 5. The walls of the blood vessel 5 define a lumen boundary.This lumen boundary can be detected using the distance measurementsobtained from the optical signals collected at the probe tip 17 usinglumen detection software component. Side branches and stent struts andshadow regions and other features can be identified in the scan linesgenerated during a pullback through the artery by the probe.

In one embodiment, the probe 7 can include other imaging modalities inaddition to OCT such as ultrasound in one embodiment. In one embodiment,the lumen/lumen boundary refers to a portion of the vessel that is firstimpinged upon when light or ultrasound exists an intravascular imagingprobe that generates a signal of interest for imaging the vessel. Thisexcludes any blood flowing in the vessel which is typically removedusing image processing in the form of masking. In one embodiment, thelumen or lumen boundary refers to a region of tissue that is disposed infront of the vessel wall and facing the blood containing region of thevessel.

As shown in FIG. 10A, the probe tip 17 is positioned in the lumen L suchthat it is distal to a stented region of the blood vessel 5. The probetip 17 is configured to transmit light and receive backscattered lightfrom objects, such as for example stent 12, and the wall of the bloodvessel 5. The probe tip 17 and the rest of the data collection probe 7are pulled through the lumen L and the stented region. As shown in FIG.10B, a probe 17 is shown prior to or after insertion in a blood vessel.The probe 7 is in optical communication with an OCT system 10. The OCTsystem or subsystem 10 that connects to probe 17 via an optical fiber 15can include a light source such as a laser, an interferometer having asample arm and a reference arm, various optical paths, a clockgenerator, photodiodes, and other OCT system components.

In one embodiment, an optical receiver 31 such as a balanced photodiodebased system can receive light exiting the probe 7. A computing device40 such as a computer, processor, ASIC or other device can be part ofthe OCT system 10 or can be included as a separate subsystem inelectrical or optical communication with the OCT system 10. Thecomputing device 40 can include memory, storage, buses and othercomponents suitable for processing data and software 44 such as imagedata processing stages configured for side branch detection, stent strutcandidate selection or identification, candidate stent strut shadowregion detection, stent region detection, stent strut validation,correlations and comparisons of stent image data stent visualization,and pullback data collection as discussed below. In one embodiment, thesoftware 44 can include a pipeline that includes various modules such asa stent detection module that is automated such that is operates onintravascular data to detect stent struts. The module can includevarious other software modules such as a sparse peak detection module,model strut generation module, false positive testing module, and othersas described herein.

In one embodiment, the computing device 40 includes or accesses softwaremodules or programs 44, such as a side branch detection module, a lumendetection module, a stent detection module, a stent strut validationmodule, a candidate stent strut identification module and other softwaremodules. The software modules or programs 44 can include an image dataprocessing pipeline or component modules thereof and one or moregraphical user interfaces (GUI). The various software-based methodsdescribed herein can be included as part of the group ofsoftware/programs 44. The modules can be subsets of each other andarranged and connected through various inputs, outputs, and dataclasses. In one embodiment, the software modules 44 include a stentdetection module such as an automated stent detection module.

An exemplary image processing pipeline and components thereof canconstitute one or more software programs or modules 44. The softwaremodules 44 may comprise several image processing algorithms tailored todetect the vessel lumen, side-branches, guide-wires, guide-catheters,stent struts and stent regions. This disclosure relates to imageprocessing to determine the location of a metal strut within its shadow.The image data processing pipeline, its components software modules andrelated methods and any of the methods described herein are stored inmemory and executed using one or more computing devices such as aprocessor, device, or other integrated circuit. The software modules orprograms 44 receive image data and transform such image data into twodimensional and three dimensional views of blood vessels and stents andcan include lumen detection software module, peak detection, stentdetection software module, and side branch detection software modulesand others.

As shown, in FIG. 10A, a display 46 can also be part of the system 10for showing information 47 such as cross-sectional and longitudinalviews of a blood vessel generated from OCT or IVUS imaging data andapposition bars and other indicators. The image processing softwarealgorithms 44 provide data corresponding to detected image features suchas stents, side-branches, guide-wire etc. and this data is input to theGUI where these features are displayed in a desired format oncross-sectional, longitudinal, and/or 3D display sections of the GUI.

In addition, the display 46 can also show information 47 such ascross-sectional and longitudinal views of a stented blood vesselgenerated using collected image data, user interfaces, images andvarious indicators and indicia. Representations of a stent, such as OCTor IVUS images thereof, can be shown to a user via display 46. Stentdetection is performed prior to the display of these features and anycoding or tagging with identifying indicia that may be included in thedisplayed image. This OCT-based information 47 can be displayed usingone or more graphic user interface(s). The images of FIGS. 10B, 14A,14B, and 18B and the other user interfaces and the components thereofdepicted herein are examples of display information 47 that can bedisplayed and interacted with using a GUI and various input devices.Specifically, it shows a 2D cross-sectional view of a coronary arterycontaining a metal stent.

In addition, display information 47 can include, without limitation,cross-sectional scan data, longitudinal scans, diameter graphs, imagemasks, stents, areas of malapposition, lumen border, and other images orrepresentations of a blood vessel or the underlying distancemeasurements obtained using an OCT system and data collection probe. Thecomputing device 40 can also include software or programs 44, which canbe stored in one or more memory devices 45, configured to identify stentstruts and malapposition levels (such as based on a threshold andmeasured distance comparison), shadow regions, and struts within shadowregions and other blood vessel features such as with text, arrows, colorcoding, highlighting, contour lines, or other suitable human or machinereadable indicia. Once the OCT data is obtained with a probe and storedin memory; it can be processed to generate information 47 such as across-sectional, a longitudinal, and/or a three-dimensional view of theblood vessel along the length of the pullback region or a subsetthereof. These views can be depicted as part of a user interface asshown in FIGS. 1B, 1C and 14A and 14B for example and as otherwisedescribed and depicted herein.

FIG. 10B is a cross-sectional OCT image of a stented blood vessel, inaccordance with the present disclosure. The lumen/lumen boundary L ofthe blood vessel is in the center of the image. The guide wire shadow 12is visible at the top of the image, from 12 to 1 o'clock. Also visiblein FIG. 10B are multiple metal stent struts 14, which cast shadows 16 inthe OCT image. Metal stent struts cast shadows against the blood vesselwall because the coherent light typically used for OCT imaging cannotpenetrate stent struts but is reflected. The present disclosure providesenhanced methods for detecting the precise offset of struts within strutshadows. Once detected, the shadows and struts of FIG. 10B can be usedto generate the user interfaces and indicators described herein.

FIG. 11 is a process flow chart for detecting struts in OCT image data.The method 100 analyzes shadows corresponding to stent struts in aplurality of OCT pullback frames. The method 100 can include one or moreof the steps described herein. Unless otherwise required, the steps canbe performed in any order. The metal strut detection method operatesupon various inputs from other image/intravascular data processingmodules such as information about guide-wire (140), side-branch (130)and strut shadow locations (110). The process flow and associated methodsteps and stages can operate upon original intravascular data or rawdata 120 obtained using a OCT, IVUS, or other intravascular datacollection system. In one embodiment, data 120 has been processed by oneor more image processing modules in a pipeline configuration.

In Step 110, each shadow in the OCT image data is compared or correlatedwith data inputs from a side branch detection module 130 and aguide-wire detection module 140 to determine if the shadow isattributable to a side branch vessel or guidewire. Methods, systems, anddevices for detecting strut shadows, side branches, and pidewire shadowsare known. See, e.g., U.S. Pat. Nos. 8,412,312; 8,478,387; 8,831,321;9,138,147 and 9,173,591.

At Step 150, if a given shadow is determined to be attributable to theguidewire or a side branch, the shadow is discarded and the analysisends with respect to that shadow. At Step 160, if a given shadow isdetermined to be attributable to a stent strut, either by directdetection or by process of elimination, the shadow is analyzed tocompute, or isolate, the interior part of the shadow. The shadowboundaries are trimmed away or otherwise reduced or constrained suchthat only the scan lines, or A-Lines, corresponding to the interior (andhence the “darkest”) portion of the shadow are retained. The reason forthis is that the shadow region, specifically the start and stop scanlines of the shadow, can sometimes contain spillage from the neighboringlumen pixels. Isolating the interior of the shadow and ignoringtransitionary scan lines at the shadow margins improves assessment ofstrut offsets.

At step 170, the shadow interior is analyzed to compute the projection(or sum) of each sample across scan lines corresponding to the interiorpart of the shadow. Each scan-line is sampled into discrete pixels or“samples”. In the input OCT image data, each scan line refers to dataacquired along a particular angular direction with the imaging catheterat the center. Each scan line is in turn radially sampled into adiscrete set of pixels or “samples”. Each sample in the OCT data istypically a few microns wide and is typically uniform in size. A“projection” refers to the process of adding across each scan line. Inother words, the 2-dimensional shadow in the {scan-line, sample} spaceis collapsed into a 1-dimensional signal where the i-th indexcorresponds to the sum of the i-th sample of each scan-line involved inthe process. The projection contains samples, at radius R, which are theaverage of samples from the constituent scanlines at that same radius R.

At step 180, the projection is searched for up to three (e.g., 1, 2, or3) of the largest local maxima. The location, or offset, of eachselected maximum may be noted as potential strut locations, and certainfeatures of the selected maxima are then analyzed to determine which oneis the best candidate for being a true strut. In various embodiments,only the largest maximum is selected. In other embodiments, two or threeof the largest maxima are selected. The initial selection of multiplelocal maxima increases sensitivity. Although more than three localmaxima can be selected, this typically is unnecessary because one of thethree highest maxima usually indicates the true strut location. Themaxima selection process is illustrated by FIGS. 12 and 13 .

FIG. 12 is an A-Line or scan line OCT image, in log scale, of a stentedblood vessel. The box 155 on the right of the image indicates a shadowunder analysis, and FIG. 13 shows a projection graph for this shadow.The blood vessel lumen L is the dark region at the top of the image, andthe blood vessel wall VW is the bright region at the bottom of theimage. L is generally used to indicate the lumen herein. Multiple stents14 and stent shadows 16 are visible in the image. In one embodiment, thelumen is the boundary between the tissue and the cleared interior of thevessel.

FIG. 13 is a graph illustrating detection of multiple potential strutswithin a single shadow. FIG. 13 is a plot of the projection across theinterior shadow scan lines. There are two local maxima 22 a and 22 b.These maxima correspond to two potential strut locations within theshadow. These locations can be used by an intravascular imaging systemsuch as an OCT or IVUS system to display stent struts on a userinterface as shown herein.

Additional filters can be applied to the local maxima to remove falsepositives. In various embodiments, a local maximum is selected only ifit has a signal greater than 1/10^(th) (i.e., 10%) of the global peak(largest maximum along the projection. The global peak is the peak withthe greatest amplitude. The 10% threshold reduces the chance ofselecting spurious local maxima due to noise. The threshold can be setbetween 5% (i.e., 1/20th) and 10% of the global peak, such as 5%, 6%,7%, 8%, 9%, or 10%, with 10% being preferred. In various embodiments, ifmultiple peaks are detected in close proximity to each other, only thelargest peak is selected for further analysis.

At Step 190, the selected local maxima are analyzed to determine whichmaximum has the highest probability of being the true strut based on theinformation available from the immediate neighborhood of the strut. Arelative score is assigned to each strut based on one or more of thefollowing criteria:

1. Proximity to lumen: The selected local maxima are scored based onproximity to the lumen boundary. The local maximum closest to the lumenaround the strut shadow receives the highest score, and the localmaximum farthest from the lumen around the strut shadow receives thelowest score.

2. Peak strength: The selected local maxima are scored based on peakstrength. The local maximum with the highest peak receives the highestscore, and the local maximum with the lowest peak receives the lowestscore.

3. Degree of malapposition: The selected local maxima are scored basedon their apposition, which refers to the state of being in juxtapositionto the lumen. Local maxima that are apposed within a predefinedacceptable distance from the lumen or vessel wall receive a highermalapposition score. Struts that are too far away from the lumen orvessel wall (determined by a user specified threshold using one or moreinterface screens or based on accepted treatment thresholds) arepenalized and receive a lower malapposition score as potential falsepositives. In one embodiment, a strut can either have a malappositionscore of 0 or 1 depending on whether it is malapposed or not,respectively.

These scoring criteria are exemplary, and additional scoring criteriabased on other strut and shadow features may be used. In one embodiment,candidate stent struts are validated using a cross-frame analysis toindicate that a strut is valid if a segment of strut is next to oraligned with another segment in an adjacent or neighboring frame.

Each local maxima gets a combined score which is the linear sum of theabovementioned criteria. At Step 200, the local maximum with the highestscore is selected as the valid strut. At Step 210, the remaining localmaxima are saved as alternative or backup struts pending furtheranalysis. In the event of a tie, the local maximum closest to the lumenand/or the brightest local maximum are used as tiebreakers. Table 1provides an exemplary ranking of local maxima for a stent shadow.

TABLE 1 Local maxima ranking for a stent shadow. Local Local LocalMaximum 1 Maximum 2 Maximum 3 Proximity to Lumen 3 2 1 Peak Strength 2 13 Malapposition 1 0 0 Total 6 3 4

As shown in Table 1, local maximum 1 has the highest total score andtherefore would be selected as the candidate valid strut. Local maxima 2and 3 would be designated as backup struts.

At Step 220, all local maxima (valid strut and any backup struts)undergo multi-frame validation. In this step, adjacent frames arecompared to verify that a valid strut in one frame aligns with validstruts selected for adjacent frames. If a valid strut does not alignswith other cross-frame struts, then the valid strut may be replaced by abackup strut if the backup strut better fits the cross-frame model. Oneembodiment of the multi-frame validation step can use stent strutgeometry and location information. Other embodiments with a larger setof strut and shadow features can also be used for this step. That islocation and geometry can be used as features all with other featuressuch as prior pullback data or other user supplied information.

Once detected, the valid or chosen struts can be displayed on a userinterface, which conveys vital visual aid to the clinician about theprecise location of stent struts and whether adjustments may benecessary to optimize and/or speed-up stent placement and reduce therisk of side effects. The user interface can include cross-sectionalimages, L-Mode images, scan line images, three dimensional renderings,or any other suitable display format for visualizing detected struts.The user interface can also include the indicator bars, angiographydata, and other views and features described and depicted herein.

The detection algorithm accurately identified the location of struts,with a sensitivity of that ranges from greater than about 80% in oneembodiment. The detection algorithm accurately identified the locationof struts, with a sensitivity of that ranges from greater than about 00%in one embodiment. In one embodiment, sensitivity is the proportion ofstruts correctly located over the total number of struts (strutscorrectly located plus struts missed). The positive predictive value isthe proportion of struts correctly detected over all positive calls(struts correctly detected plus false positive in one embodiment. Thevarious features described herein are suitable for use with differentcath lab systems such as intravascular imaging and pressure measurementsystems. The indicators and detection steps described herein offervarious advantages to diagnosticians and those planning stentdeployments or evaluating deployed stents.

In part, the invention provides computer-based methods, systems, anddevices for detecting and displaying a stented region. In particular,the invention can identify the first and last frames of a stentedregion. A frame, in this context, refers to a cross-section through thevessel being imaged via OCT. The stented region is identified byiteratively processing OCT image frames to determine whether framesand/or a neighborhood of frames show features consistent with anexpected configuration of stent struts. Stent struts appear in OCTimages as solid structures, which are distinguishable from soft tissues,such as a blood vessel wall. In addition, the struts of a properlyinflated stent typically are located adjacent the blood vessel wall.Thus, in a cross section of stented blood vessel, a plurality of stentstruts would be distributed uniformly around the circumference of thevessel wall. Thus, frames exhibiting OCT features consistent with adeployed stent—e.g., a plurality of struts distributed around the entirevessel wall—are candidates for being designated as stent-containingframes.

In OCT imaging data, artifacts due to uncleared blood cells or catheterwalls can have similar optical properties as stent struts. These imagingartifacts can often be interpreted by software as stent struts. However,background noise typically does not have the regular geometry of stents,which are composed of a meshwork of struts. For example, uncleared bloodcells may be clustered in a single region, may be distributed randomly,and/or may not be positioned adjacent a vessel wall. The challenge is todistinguish frames that contain stent struts from frames that containonly false positives due to artifacts and no stent struts.

Another challenge is distinguishing whether there is a valid stentedregion present in the OCT image date and, if so, correctly locating thefirst and last frame imaging frames containing the stent. In oneembodiment, the methods described herein are applicable to metal stentsand bioresorbable stents and other non-metal stents. In general, one ormore embodiments of the disclosure provide methods to identify frames ofan intravascular pullback and the associated representation displayed toan end user that accurately detects stent struts and also accuratelyidentifies zones or regions of the blood vessel in which no stent ispresent.

Stent Detection Embodiments

In part, the invention provides computer-based methods, systems, anddevices for detecting and displaying a stented region. In particular,the invention can identify the first and last frames of a stentedregion. A frame, in this context, refers to a cross-section through thevessel being imaged via OCT. The stented region is identified byiteratively processing OCT image frames to determine whether framesand/or a neighborhood of frames show features consistent with anexpected configuration of stent struts. Stent struts appear in OCTimages as solid structures, which are distinguishable from soft tissues,such as a blood vessel wall. In addition, the struts of a properlyinflated stent typically are located adjacent the blood vessel wall.Thus, in a cross section of stented blood vessel, a plurality of stentstruts would be distributed uniformly around the circumference of thevessel wall. Thus, frames exhibiting OCT features consistent with adeployed stent for e.g., a plurality of struts distributed around theentire vessel wall are candidates for being designated asstent-containing frames.

In OCT imaging data, artifacts due to uncleared blood cells or catheterwalls can have similar optical properties as stent struts. These imagingartifacts can often be interpreted by software as stent struts. However,background noise typically does not have the regular geometry of stents,which are composed of a meshwork of struts. For example, uncleared bloodcells may be clustered in a single region, may be distributed randomly,and/or may not be positioned adjacent a vessel wall. The challenge is todistinguish frames that contain stent struts from frames that containonly false positives due to artifacts and no stent struts.

Another challenge is distinguishing whether there is a valid stentedregion present in the OCT image date and, if so, correctly locating thefirst and last frame imaging frames containing the stent. In oneembodiment, the methods described herein are applicable to metal stentsand bioresorbable stents and other non-metal stents. In general, one ormore embodiments of the disclosure provide methods to identify frames ofan intravascular pullback and the associated representation displayed toan end user that accurately detects stent struts and also accuratelyidentifies zones or regions of the blood vessel in which no stent ispresent.

FIG. 14A is an L-Mode, or longitudinal, display of a stented vesselregion before elimination of false positive struts. The longitudinaldisplay is a type of cross-sectional view. Distal (D) end is to the leftand proximal (P) end is to the right of the vessel depicted in theL-mode image. The actual stented region 101 spans from about 12 mm toabout 45 mm in the L-Mode image. A false positive region 15 is locatedimmediately adjacent the actual stented region 101, from about 45 mm toabout 52 mm in the L-Mode image. The false positive region 15 is causedby background noise that has similar optical properties as stent struts.For example, uncleared blood cells swirling in the imaging areasometimes appear as stent struts in OCT images.

A software program analyzing these imaging data typically includes thefalse positive region 15 as part of the stented region 101. As a result,the software program may determine that the first stented frame isaround 12 mm and the last stented frame is around 52 mm Display of thefalse positive region 15 as part of the stented region 101 can lead tomisinterpretation or confusion in understanding of the image by theclinician, which can further lead to failed intervention (e.g., failureto reposition a malapposed stent) or unnecessary procedures (e.g.,repositioning a properly deployed stent).

FIG. 14B is an L-Mode display of a stented vessel region afterelimination of non-stented region 15, in accordance with the invention.This invention does not detect individual false positive struts butrather looks at the distribution of detected struts over a fixedlongitudinal neighborhood and assesses the location of the stentedregion. False positive stented regions 15 can be eliminatedautomatically, enabling detection of the actual stented region 101.

FIG. 15A is a diagrammatic depiction of one embodiment of the stentregion detection algorithm that creates a neighborhood of framesrelative to frame being evaluated such as a frame k. In one embodiment,the neighborhood of frames can include two adjacent frames k and k+1 (ork and k−1) are used to analyze the strut angular coverage and geometryat frame k. The size and rules associated with selecting frames for aneighborhood can vary for a given application. In one embodiment, thestent region detection algorithm uses frames k−1, k and k+1 toaccumulate struts and determine the max angular gap and subsequently theangular coverage metric. For a given neighborhood created relative toframe k, the set of frames on either side of k can be summed to create asuper frame. A given super frame is an amalgamation of the struts onframe k and the other struts on the frames in the neighborhood.

In one embodiment, stent struts 50 are detected in OCT image data usingknown techniques and an estimate of the centroid 54 of the vessel wallis also pre-computed by known methods and used by the algorithm. If thestent is properly deployed and expanded, the stent struts typically willbe adjacent the blood vessel wall 52 at the luminal boundary, but thismethod is equally applicable to frames where the stent deployment is notproperly apposed against the lumen boundary (vessel wall). For a givenframe k, we combine its struts with struts detected over a fixedneighborhood (in this embodiment, the neighboring frame k+1). Theangular position of each strut is determined using the vessel centroid.

FIG. 15B is a diagrammatic depiction of another exemplary embodiment ofthe stent region detection algorithm where a neighborhood of frames isused to create a superframe or an amalgamation of struts for theneighborhood center on frame k. As shown it FIG. 15B, three adjacentframes k−1, k, and k+1 are used to analyze the strut angular coverageand geometry in a 3 frame neighborhood. As in FIG. 15A, stent struts 50are detected in OCT image data using known techniques and an estimate ofthe centroid of the vessel wall is also pre-computed by known methodsand used by the algorithm.

If the stent is properly deployed and expanded, the stent strutstypically will be adjacent the blood vessel wall 52 at the luminalboundary, but this method is equally applicable to frames where thestent deployment is not properly apposed against the lumen boundary(vessel wall). For a given frame k, we combine its struts with strutsdetected over a fixed neighborhood (in this embodiment, the neighboringframes k−1 and k+1). The angular position of each strut is determinedusing the vessel centroid.

If a given frame belongs to a stented region, the frame should containstruts having close to a 360-degree coverage around the circumference ofthe blood vessel wall 52. Occasionally, struts are missed during the OCTimaging process, which would appear as a gap in coverage around thelumen. Thus, in a preferred embodiment, stent information from multipleframes is stacked or combined, and the gaps between struts in thecombined data are then measured. Using struts detected over aneighborhood helps smooth the plot and use an easier thresholding methodto separate the true stent regions from false positives. Also, presenceof guide-wires and side-branches which manifest as large shadows in theimage can lead to a lower angular coverage around the circumference.These features can be identified and can be accounted for when analyzinggaps between stent struts.

Referring again to FIGS. 15A and 15B, strut information from frame k isstacked or combined with stent information from frame k+1 to createmulti-frame stent data. In the multi-frame analysis, the orientation ofeach frame is preserved, and the angular gap between struts is measuredaround the circumference of the vessel lumen in the multi-frame data.The largest angular gap between adjacent struts, θ_(max,k), is thencomputed relative to the vessel centroid for frame k.

FIG. 15A depicts a multi-frame analysis based on two adjacent frames, kand k+1. FIG. 15B depicts a multi-frame analysis based on three adjacentframes, k−1, k, and k+1. Combining struts from the adjacent framesbrings in cross frame information to the stent region detection method.False positive struts will tend to be randomly distributed across aframe; therefore, frames and multi-frames containing only falsepositives are unlikely to show a uniform strut coverage around thecircumference of the blood vessel. Hence, the largest angular gap for avalid stented frame or multi-frame is smaller, often much smaller, thanthe largest angular gap for a non-stented frame or multi-frame.

While larger neighborhoods can be used, the presence of false positivesin larger neighborhoods can interfere with the approach. Thus, smallneighborhood (e.g., 2-3 frames) are preferred. In various embodiments,the algorithm analyzes OCT data from a pullback to identify all framesand/or frame neighborhoods that contain a stent—i.e., where θ_(max,k)falls within a range values expected of a stented region. Usingcross-frame and cross-neighborhood analyses, the algorithm determinesthe first and last stented frames of a stented region. False positivesoutside the stented region are revealed because the false positives arenot contiguous with the stented region.

False positives around a stent end can be further eliminated bycomparing the detected length of the stent to the known actual stentlength. If the detected stent length exceeds the known stent length, thedetection algorithm can be refined by either using a dynamic thresholdto adjust the stent region or adjust the size of the neighborhood togive a better estimate of the stent region. The same approach can beapplied to situations where the detected stent length is shorter thanthe known length.

The detection algorithm also can include a validation step that comparesthe detected stent geometry and length to a known stent geometry andlength. Frames exhibiting atypical geometries can be eliminated as falsepositives and/or can be deprioritized until it is apparent that theatypical frame is part of a contiguous region of frames.

The detection algorithm is not limited to analyzing pairs of frames, butalso can analyze stent information from a single frame, if the stentbeing imaged contains a sufficiently dense mesh network, or from morethan two frames (e.g., 3, 4, 5, or more) if the stent being imagedcontains a sparse mesh network. In addition, the frames used in themulti-frame analysis need not be adjoining frames but could be separatedby a few frames. In one embodiment, this can be implemented using asliding widow type algorithm.

The maximum angular gap is used to derive an angular coverage metric foreach multi-frame, and the angular coverage metric can be plotted versusframe number on a graph. FIG. 16 is graph of an angular coverage plotdemarcating the frame location of two stents within OCT pullback data.The angular coverage metric, Ψ_(k) for frame k is defined by thefollowing equation:

$\Psi_{k} = {1 - \frac{\theta_{maxk}}{2\pi}}$where θ_(max k) is the measured maximum angular gap for frame k. Themeasured maximum angular gap for frames k−1 and k+1 also are calculated.For each frame being evaluated using the stent region detection method,strut information from adjacent frames is combined. That said, theangular coverage metric is relative to the current frame k and appliesto frame k even if a neighborhood of frames is summed and evaluated forcoverage, the coverage result is generated for all of the frames in oneembodiment. As angular gap size increases, the angular coverage metricdecreases. In preferred embodiment, an angular coverage metric thresholdis used to classify frames as either containing a stent or not.

A frame having an angular coverage metric below the predeterminedthreshold is categorized as a non-stented frame, whereas a frame havingan angular coverage metric above the predetermined threshold iscategorized as a stented frame. In a preferred embodiment, the angularcoverage metric threshold is, for example, from about 0.25 to about0.65. The angular coverage threshold can be automatically set orcomputed dynamically by the software, or it can be user defined, forexample, depending on the geometry of particular stent. In oneembodiment, about 0.8 is the angular coverage metric threshold valueseen for some stents. In one embodiment, less than about 0.3 is theangular coverage metric threshold value seen when the frame k is outsideof a stented region.

FIG. 16 is angular coverage plot for an illustrative OCT pullback. Theangular coverage metric threshold is set at 0.4. The angular coveragemetric is below 0.4 for frames 0 to 20; thus, these frames arecategorized as non-stented frames by the algorithm. The angular coveragemetric for frames 0 to 20 is less than zero, indicating potentialbackground noise. Frames 20 to 125 have an angular coverage metricbetween 0.7 and 0.9, well above the threshold of 0.4. Frames 20 to 125therefore are categorized as stented frames by the algorithm. Theangular coverage metric drops sharply to zero after frame 125 andremains below the threshold until frame 140, indicating a non-stentedregion between frames 125 and 140. At frame 140, the angular coveragemetric increases above the 0.4 threshold and remains above the thresholdfrom frames 140 to 220. Thus, frames 140 to 220 are categorized as astented region.

FIG. 17 is a flow chart showing a stent region detection algorithm 201.As will be appreciated, additional steps or analyses can be introducedwithout departing from base detection algorithm. The detection algorithm200 receives guide catheter data 211 and/or stent strut detection data212 obtained from preliminary analysis of OCT pullback data. These inputdata often are based on analysis of OCT scan lines or single OCT frames.At step 214, these input data are used to eliminate frames and/or stentstrut detections in the guide catheter region. This step is optional butis preferred because the guide catheter often produces imaging artifactsthat are misinterpreted as stent struts.

Next, at step 216, cross-frame or multi-frame information is generatedby combining strut data from neighboring frames, k and k+1. In this way,the strut data for one frame that is near other frames in a neighborhoodcan be amalgamated, aggregated or combined to perform a type ofcross-frame validation. In a preferred embodiment, frames k and k+1 areimmediately adjoining OCT frames. In another embodiment, a neighborhoodof 2n+1 frames can be used using frame set {k−n, k, k+1, . . . k−1, k,k+n−1, k+n}. For n=1, frames k−1, k and k+1 are used. However, as notedabove, frame k struts can be combined with a data from a frame severalmicrons apart. In addition, as noted above, the detection algorithm cananalyze stent information from a single frame.

At step 218, the detection algorithm computes the angular gap betweenconsecutive struts around the periphery of the vessel lumen in themulti-frame data. At step 221, the maximum angular gap for a givenmulti-frame is determined. The maximum angular gap is then used at step222 to compute an angular gap metric for each frame, etc.). At step 224,the angular gap metric is then compared against a threshold angular gapmetric. If the angular gap metric for a given multi-frame exceeds thethreshold angular gap metric, then that multi-frame is flagged forinclusion in the actual stented region. Finally, at step 226, thedetection algorithm determines, based on the multi-frame analysis, whichOCT frames correspond to an actual stented region. Validation step 228can be performed. The method can further include displaying an indiciarelative to a region of a blood vessel indicative of a stented region.In general, any of the detected and validated struts can be displayed asdescribed and depicted herein. Further, in one embodiment, the indiciais an apposition bar aligned with the stent region, wherein theapposition bar is rotationally agnostic or persistent.

FIG. 18A is user interface representation showing a longitudinal orL-Mode display of a stented vessel region 101 before elimination offalse positive struts 18. Multiple false positive struts were detectedaround 20 mm and around 34 mm FIG. 18B is an L-Mode display of a stentedvessel region 101 after elimination of false positive struts. As shownin FIG. 18B, the detection algorithm eliminates false positive struts,resulting in a more accurate display of the actual stented region.

The use of arrow heads showing directionality in a given figure or thelack thereof are not intended to limit or require a direction in whichinformation can flow. For a given connector, such as the arrows andlines shown connecting the elements shown in FIGS. 1 and 10A, forexample, information can flow in one or more directions or in only onedirection as suitable for a given embodiment. The connections caninclude various suitable data transmitting connections such as optical,wire, power, wireless, or electrical connections.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations can be used by those skilled in the computer andsoftware related fields. In one embodiment, an algorithm is here, andgenerally, conceived to be a self-consistent sequence of operationsleading to a desired result. The operations performed as methods stopsor otherwise described herein are those requiring physical manipulationsof physical quantities. Usually, though not necessarily, thesequantities take the form of electrical or magnetic signals capable ofbeing stored, transferred, combined, transformed, compared, andotherwise manipulated.

Non-Limiting Software Features and Embodiments for ImplementingInterface, Detection and Other Features of Disclosure

The following description is intended to provide an overview of devicehardware and other operating components suitable for performing themethods of the disclosure described herein. This description is notintended to limit the applicable environments or the scope of thedisclosure. Similarly, the hardware and other operating components maybe suitable as part of the apparatuses described above. The disclosurecan be practiced with other system configurations, including personalcomputers, multiprocessor systems, microprocessor-based or programmableelectronic device, network PCs, minicomputers, mainframe computers, andthe like. The disclosure can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network such as in different roomsof a catheter or cath lab.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations can be used by those skilled in the computer andsoftware related fields. In one embodiment, an algorithm is here, andgenerally, conceived to be a self-consistent sequence of operationsleading to a desired result. The operations performed as methods stopsor otherwise described herein are those requiring physical manipulationsof physical quantities. Usually, though not necessarily, thesequantities take the form of electrical or magnetic signals capable ofbeing stored, transferred, combined, transformed, compared, andotherwise manipulated.

Unless specifically stated otherwise as apparent from the followingdiscussion, it is appreciated that throughout the description,discussions utilizing terms such as “processing” or “computing” or“searching” or “indicating” or “detecting” or “measuring” or“calculating” or “comparing” “generating” or “sensing” or “determining”or “displaying,” or Boolean logic or other set related operations or thelike, refer to the action and processes of a computer system, orelectronic device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system's orelectronic devices' registers and memories into other data similarlyrepresented as physical quantities within electronic memories orregisters or other such information storage, transmission or displaydevices.

The present disclosure, in some embodiments, also relates to apparatusfor performing the operations herein. This apparatus may be speciallyconstructed for the required purposes, or it may comprise a generalpurpose computer selectively activated or reconfigured by a computerprogram stored in the computer. Various circuits and components thereofcan be used to perform some of the data collection and transformationand processing described herein.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description below.In addition, the present disclosure is not described with reference toany particular programming language, and various embodiments may thus beimplemented using a variety of programming languages. In one embodiment,the software instructions are configured for operation on amicroprocessor or ASIC of an intravascular imaging/data collectionsystem.

Embodiments of the disclosure may be embodied in many different forms,including, but in no way limited to, computer program logic for use witha processor (e.g., a microprocessor, microcontroller, digital signalprocessor, or general purpose computer), programmable logic for use witha programmable logic device, (e.g., a Field Programmable Gate Array(FPGA) or other programmable logic device), discrete components,integrated circuitry (e.g., an Application Specific Integrated Circuit(ASIC)), or any other means including any combination thereof. In atypical embodiment of the present disclosure, some or all of theprocessing of the data collected using an OCT probe and theprocessor-based system or used to generate a control signal or initiatea user interface command is implemented as a set of computer programinstructions that is converted into a computer executable form, storedas such in a computer readable medium, and executed by a microprocessorunder the control of an operating system.

Thus, query, response, transmitted probe data, input data and other dataand signal described herein are transformed into processorunderstandable instructions suitable for responding to user interfaceselections, controlling a graphical user interface, control and graphicsignal processing, displaying cross-sectional information and imagesfrom other data collection modalities, generating and displaying stentsand apposition bars and other intravascular data, displaying OCT,angiography, detecting shadows, detecting peaks, and other data as partof a graphic user interface and other features and embodiments asdescribed above. Data and parameters suitable for display as GUIcomponents or controls, values, or as another representation in agraphical user interface can include without limitation malappositionvalues, apposition bars, stent struts, missing data representations,indicator bars, shadows, angiography representations, three and twodimensional renders and views, and other features as described herein.

Computer program logic implementing all or part of the functionalitypreviously described herein may be embodied in various forms, including,hut in no way limited to, a source code form, a computer executableform, and various intermediate forms (e.g., forms generated by anassembler, compiler, linker, or locator). Source code may include aseries of computer program instructions implemented in any of variousprogramming languages (e.g., an object code, an assembly language, or ahigh-level language such as Fortran, C, C++, JAVA, or HTML) for use withvarious operating systems or operating environments. The source code maydefine and use various data structures and communication messages. Thesource code may be in a computer executable form (e.g., via aninterpreter), or the source code may be converted (e.g., via atranslator, assembler, or compiler) into a computer executable form.

The computer program may be fixed in any form (e.g., source code form,computer executable form, or an intermediate form) either permanently ortransitorily in a tangible storage medium, such as a semiconductormemory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-ProgrammableRAM), a magnetic memory device (e.g., a diskette or fixed disk), anoptical memory device (e.g., a CD-ROM), a PC card (e.g., PCMCIA card),or other memory device. The computer program may be fixed in any form ina signal that is transmittable to a computer using any of variouscommunication technologies, including, but in no way limited to, analogtechnologies, digital technologies, optical technologies, wirelesstechnologies, networking technologies, and internetworking technologies.The computer program may be distributed in any form as a removablestorage medium with accompanying printed or electronic documentation(e.g., shrink-wrapped software), preloaded with a computer system (e.g.,on system ROM or fixed disk), or distributed over a network.

Hardware logic (including programmable logic for use with a programmablelogic device) implementing all or part of the functionality previouslydescribed herein may be designed using traditional manual methods, ormay be designed, captured, simulated, or documented electronically usingvarious tools, such as Computer Aided Design (CAD), a hardwaredescription language (e.g., VHDL or AHDL), or a PLD programming language(e.g., PALASM, ABEL, or CUPL).

Prop ammable logic may be fixed either permanently or transitorily in atangible storage medium, such as a semiconductor memory device (e.g., aRAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a magnetic memorydevice (e.g., a diskette or fixed disk), an optical memory device (e.g.,a CD-ROM), or other memory device. The programmable logic may be fixedin a signal that is transmittable to a computer using any of variouscommunication technologies, including, but in no way limited to, analogtechnologies, digital technologies, optical technologies, wirelesstechnologies (e.g., Bluetooth), networking technologies, andinternetworking technologies. The programmable logic may be distributedas a removable storage medium with accompanying printed or electronicdocumentation (e.g., shrink-wrapped software), preloaded with a computersystem (e.g., on system ROM or fixed disk), or distributed from a serveror electronic bulletin board over the communication system (e.g., theInternet or World Wide Web).

Various examples of suitable processing modules are discussed below inmore detail. As used herein a module refers to software, hardware, orfirmware suitable for performing a specific data processing or datatransmission task. Typically, in a preferred embodiment a module refersto a software routine, program, or other memory resident applicationsuitable for receiving, transforming, routing and processinginstructions, or various types of data such as OCT scan data, userinterface data, control signals, angiography data, user actions,frequencies, interferometer signal data, detected stents, candidatestent struts, FFR data, IVUS data, shadows, pixels, intensity patterns,scores, projections, side branch data, and guidewire data and otherinformation of interest as described herein.

Computers and computer systems described herein may include anoperatively associated machine-readable medium such as computer-readablemedia such as memory for storing software applications used inobtaining, processing, storing and/or communicating data. It can beappreciated that such memory can be internal, external, remote or localwith respect to its operatively associated computer or computer system.

Memory may also include any means for storing software or otherinstructions including, for example and without limitation, a hard disk,an optical disk, floppy disk, DVD (digital versatile disc), CD (compactdisc), memory stick, flash memory, ROM (read only memory), RAM (randomaccess memory), DRAM (dynamic random access memory), PROM (programmableROM), EEPROM (extended erasable PROM), and/or other likecomputer-readable media.

In general, computer-readable memory media applied in association withembodiments of the disclosure described herein may include any memorymedium capable of storing instructions executed by a programmableapparatus. Where applicable, method steps described herein may beembodied or executed as instructions stored on a computer-readablememory medium or memory media. These instructions may be softwareembodied in various programming languages such as C++, C, Java, and/or avariety of other kinds of software programming languages that may beapplied to create instructions in accordance with embodiments of thedisclosure.

The term “machine-readable medium” or “computer-readable-medium”includes any medium that is capable of storing, encoding or carrying aset of instructions for execution by the machine and that cause themachine to perform any one or more of the methodologies of the presentdisclosure. While the machine-readable medium is shown in an exampleembodiment to be a single medium, the term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., adatabase, one or more centralized or distributed databases and/orassociated caches and servers) that store the one or more sets ofinstructions.

A storage medium may be non-transitory or include a non-transitorydevice. Accordingly, a non-transitory storage medium or non-transitorydevice may include a device that is tangible, meaning that the devicehas a concrete physical form, although the device may change itsphysical state. Thus, for example, non-transitory refers to a deviceremaining tangible despite this change in state.

The aspects, embodiments, features, and examples of the disclosure areto be considered illustrative in all respects and are not intended tolimit the disclosure, the scope of which is defined only by the claims.Other embodiments, modifications, and usages will be apparent to thoseskilled in the art without departing from the spirit and scope of theclaimed disclosure.

The use of headings and sections in the application is not meant tolimit the disclosure; each section can apply to any aspect, embodiment,or feature of the disclosure.

Throughout the application, where compositions are described as having,including, or comprising specific components, or where processes aredescribed as having, including or comprising specific process steps, itis contemplated that compositions of the present teachings also consistessentially of, or consist of, the recited components, and that theprocesses of the present teachings also consist essentially of, orconsist of, the recited process steps.

In the application, where an element or component is said to be includedin and/or selected from a list of recited elements or components, itshould be understood that the element or component can be any one of therecited elements or components and can be selected from a groupconsisting of two or more of the recited elements or components.Further, it should be understood that elements and/or features of acomposition, an apparatus, or a method described herein can be combinedin a variety of ways without departing from the spirit and scope of thepresent teachings, whether explicit or implicit herein.

The use of the terms “include,” “includes,” “including,” “have,” “has,”or “having” should be generally understood as open-ended andnon-limiting unless specifically stated otherwise.

The use of the singular herein includes the plural (and vice versa)unless specifically stated otherwise. Moreover, the singular forms “a,”“an,” and “the” include plural forms unless the context clearly dictatesotherwise. In addition, where the use of the term “about” is before aquantitative value, the present teachings also include the specificquantitative value itself ±10%, unless specifically stated otherwise.

It should be understood that the order of steps or order for performingcertain actions is immaterial so long as the present teachings remainoperable. Moreover, two or more steps or actions may be conductedsimultaneously.

Where a range or list of values is provided, each intervening valuebetween the upper and lower limits of that range or list of values isindividually contemplated and is encompassed within the disclosure as ifeach value were specifically enumerated herein. In addition, smallerranges between and including the upper and lower limits of a given rangeare contemplated and encompassed within the disclosure. The listing ofexemplary values or ranges is not a disclaimer of other values or rangesbetween and including the upper and lower limits of a given range.

It is to be understood that the figures and descriptions of thedisclosure have been simplified to illustrate elements that are relevantfor a clear understanding of the disclosure, while eliminating, forpurposes of clarity, other elements. Those of ordinary skill in the artwill recognize, however, that these and other elements may be desirable.However, because such elements are well known in the art, and becausethey do not facilitate a better understanding of the disclosure, adiscussion of such elements is not provided herein. It should beappreciated that the figures are presented for illustrative purposes andnot as construction drawings. Omitted details and modifications oralternative embodiments are within the purview of persons of ordinaryskill in the art.

It can be appreciated that, in certain aspects of the disclosure, asingle component may be replaced by multiple components, and multiplecomponents may be replaced by a single component, to provide an elementor structure or to perform a given function or functions. Except wheresuch substitution would not be operative to practice certain embodimentsof the disclosure, such substitution is considered within the scope ofthe disclosure.

The examples presented herein are intended to illustrate potential andspecific implementations of the disclosure. It can be appreciated thatthe examples are intended primarily for purposes of illustration of thedisclosure for those skilled in the art. There may be variations tothese diagrams or the operations described herein without departing fromthe spirit of the disclosure. For instance, in certain cases, methodsteps or operations may be performed or executed in differing order, oroperations may be added, deleted or modified.

Furthermore, whereas particular embodiments of the disclosure have beendescribed herein for the purpose of illustrating the disclosure and notfor the purpose of limiting the same, it will be appreciated by those ofordinary skill in the art that numerous variations of the details,materials and arrangement of elements, steps, structures, and/or partsmay be made within the principle and scope of the disclosure withoutdeparting from the disclosure as described in the claims.

The invention claimed is:
 1. A method of determining stent strutcoverage in a blood vessel, the method comprising the steps of:receiving optical coherence tomography data for a stented blood vessel,the optical coherence tomography data comprising a plurality of imageframes; storing the optical coherence tomography data in a memory deviceof an intravascular data collection system; analyzing the plurality ofimage frames to identify stent struts on a per frame basis; combiningstrut information from a neighborhood of frames of the plurality ofimage frames to create multi-frame data; and determining, from themulti-frame data, a maximum angular gap between any two adjacent struts.2. The method of claim 1, further comprising classifying a given frameas a stent-containing frame if the maximum angular gap is smaller than athreshold angular gap.
 3. The method of claim 2, further comprisingidentifying zones that contain stents by identifying clusters ofadjacent frames containing a maximum angular gap that is smaller thanthe threshold angular gap.
 4. The method of claim 1, further comprisingdetermining a centroid value for the blood vessel and computing themaximum angular gap relative to the centroid value for a given frame k.5. The method of claim 1, wherein the maximum angular gap, θ_(max,k),for a given frame, k, is used to calculate an angular gap metric, Ψ_(k),for frame k according to a formula$\Psi_{k} = {1 - {\frac{\theta_{maxk}}{2\pi}.}}$
 6. The method of claim5, wherein an angular gap metric closer to 1 is indicative of the framecontaining a stent.
 7. The method of claim 6, further comprising thestep of iteratively calculating the angular gap metric for successiveneighboring frames.
 8. The method of claim 5, further comprisingsequentially classifying frames as a stent-containing frame if theangular gap metric for a given frame is larger than a threshold angulargap.
 9. The method of claim 1, further comprising displaying an indiciarelative to a region of the blood vessel indicative of a stented region.10. The method of claim 9, wherein the indicia is an apposition baraligned with the stented region, wherein the apposition bar isrotationally agnostic or persistent.
 11. The method of claim 1, furthercomprising: classifying a given frame as a stent-containing frame if themaximum angular gap is smaller than a threshold angular gap; andaggregating neighboring stent-containing frames into a stented regioncomprising a first frame and a last frame.
 12. The method of claim 11,further comprising terminating a first end of the stented region if aframe adjacent the first frame has an angular gap metric below thethreshold angular gap.
 13. The method of claim 11, further comprisingterminating a second end of the stented region if a frame adjacent thelast frame has an angular gap metric below the threshold angular gap.14. The method of claim 1, further comprising displaying an indiciaindicative of one or more regions in an intravascular image whereinimage data was unavailable for display.
 15. The method of claim 1,wherein an angular coverage metric is of the form${\Psi_{k} = {1 - \frac{\theta_{maxk}}{2\pi}}},$ wherein, θ_(max,k) is alargest angular gap between adjacent struts.
 16. The method of claim 15,further comprising sequentially classifying frames as a stent-containingframe if the angular coverage metric for a given frame is larger than athreshold angular gap.
 17. A method of determining stent strut coveragein a stented blood vessel, the method comprising the steps of: storing,using an intravascular imaging system, one or more intravascular imagedatasets of the blood vessel, each intravascular dataset comprising aplurality of frames; defining a neighborhood, the neighborhoodcomprising a frame k and one or more frames in vicinity of frame k;determining a maximum angular gap for frame k by combining all of strutsdetected within all frames of the neighborhood; and generating anangular coverage metric Ψ_(k) with regard to frame k using thedetermined maximum angular gap.
 18. The method of claim 17, wherein theangular coverage metric is of the form${\Psi_{k} = {1 - \frac{\theta_{maxk}}{2\pi}}},$ wherein, θ_(max,k) is alargest angular gap between adjacent struts.
 19. A programmableprocessor-based computing device of an intravascular imaging system fordetecting one or more stented regions, the programmable processor-basedcomputing device comprising: one or more data access channels to receiveintravascular imaging data; a processor and associated memory inelectrical communication with the one or more data access channels,wherein the processor is programmed to: store, using an intravascularimaging system, one or more intravascular image datasets of the bloodvessel, each intravascular dataset comprising a plurality of frames;define a neighborhood, the neighborhood comprising a frame k and one ormore frames in vicinity of frame k; determine a maximum angular gap forframe k by combining struts detected within all frames of theneighborhood; generate an angular coverage metric Ψ_(k) with regard toframe k using the determined maximum angular gap; and classify frames asa stent-containing frame if the angular coverage metric for a givenframe is larger than a threshold angular gap metric.
 20. Theprogrammable processor-based computing device of claim 19 wherein theangular coverage metric is of the form${\Psi_{k} = {1 - \frac{\theta_{maxk}}{2\pi}}},$ wherein, θ_(max,k) is alargest angular gap between adjacent struts.